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Record W3187419797 · doi:10.1098/rsbl.2021.0316

Resource exchange and partner recognition mediate mutualistic interactions between prey and their would-be predators

2021· article· en· W3187419797 on OpenAlexafffund
Luis F. Camacho, Leticia Avilés

Bibliographic record

VenueBiology Letters · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsUniversity of British Columbia
FundersSecretaría de Educación Superior, Ciencia, Tecnología e InnovaciónCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPredationBiologyHoneydewMutualism (biology)Ecology

Abstract

fetched live from OpenAlex

Animals may develop mutualistic associations with other species, whereby prey offer resources or services in exchange for protection from predators. Alternatively, prey may offer resources or services directly to their would-be predators in exchange for their lives. The latter may be the case of hemipterans that engage in mutualistic interactions with ants by offering a honeydew reward. We test the extent to which a honeydew offering versus partner recognition may play a role as proximate mechanisms deterring ants from predating upon their hemipteran partners. We showed that, when presented with a choice between a hemipteran partner and an alternative prey type, mutualist ants were less likely to attack and more likely to remain probing their hemipteran partners. This occurred even in the absence of an immediate sugary reward, suggesting either an evolved or learned partner recognition response. To a similar extent, however, ants were also less likely to attack the alternative prey type when laced with honey as a proxy for a honeydew reward. This was the case even after the honey had been depleted, suggesting an ability of ants to recognize new potential sources of sugars. Either possibility suggests a degree of innate or learned partner recognition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.276
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2021
Admission routes2
Has abstractyes

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